The present invention relates generally to material identification and, more particularly, to identify components of an unknown mixture using spectral analysis.
Material identification is performed to identify components of an unknown mixture in a wide range of scenarios such as train derailment, overturned vehicles on freeways, leaks, explosions in a chemical plant, illegal drug manufacturing labs and the like. Also, on-site identification of materials is performed in some situations by using sophisticated analytical techniques. Analytical instrumentation such as multi-wavelength infrared and Raman spectrometers, mass spectrometers, nuclear magnetic resonance (NMR) spectrometers, and chromatographic separation-detection systems are typically used for identifying unknown materials. Further, various portable instruments have been developed to provide on-site identification of materials. These instruments use an embedded algorithm for performing material identification. A library of known materials is stored on the instrument and the algorithm identifies the unknown components based on the similarity between the unknown spectra of the material and the stored spectra of the known compounds stored in the library. These methods are generally known as spectral searching methods.
Spectral searching methods typically work well when searching for pure components. However, when mixtures are analyzed using traditional spectral searching methods, the match with the stored materials in the library often indicates a poor match. These problems arise because spectrum of a mixture differs significantly from any of the spectra of pure components of the mixture. This is especially true when there is no single dominant component in the mixture. Since a typical library stores only spectra of pure components that comprise the mixture, many spectral library packages are not well suited for mixture analysis. One approach to overcome this problem is to incorporate spectra of the mixtures into the library. However, the number of possible mixtures that needs to be collected rises exponentially with the number of pure materials in the library. If we further take into account the fact that each of these mixtures would need to be collected at multiple relative concentrations of their constituents, this approach is not feasible other than for libraries of very limited size.
Another approach used for identifying components of an unknown mixture is subtraction based mixture analysis. This involves measuring the spectrum of the unknown mixture, matching the spectrum with the stored spectra of known compounds in the library, subtracting the spectrum of the top match or one of the top matches from the unknown spectrum and repeating the search on the portion of the unknown spectrum (residual spectra) that remains after the subtraction. The residual spectrum contains the spectra of other components of the mixture. In this situation, it is assumed that the top matched compound is always present in the mixture. However, in some cases, it has been observed that the top match may not be a part of the unknown spectrum. Thus, conventional subtraction based approach may not lead to a correct solution in some cases. Also, spectrum of a compound other than the top match may be subtracted thus requiring the user to decide which compound should be subtracted and making this approach impractical for many users.
It would therefore be desirable to have an efficient spectral searching method to identify components of an unknown mixture.